Pandas is famous for its datetime parsing, processing, analysis & plotting functions. It is vital to inform Python about date & time entries.
Time-series analysis and forecasting is one of the most widely applied machine learning problems. It finds applications in weather forecasting, earthquake prediction, space science, e-commerce, stock market prediction, medical sciences, and signal processing. While dealing with a time-series dataset, the data may contain the date, month, day, and time in any format. This is because people tend to use different date and time formats. Moreover, Python assumes a non-numbered entry as an object and a numbered entry as an integer or float. Hence, it is important to inform Python about the date and time entries.
In this article, we will be discussing an algorithm that helps us analyze past trends and lets us focus on what is to unfold next so this algorithm is time series forecasting. In this analysis, you have one variable -TIME. A time series is a set of observations taken at a specified time usually equal in intervals. It is used to predict future value based on previously observed data points.
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I will talk about time series basics with Pandas in this post. Time series data in different fields such as finance and economy is an important data structure. The measured or observed values over time are in a time series structure. Pandas is very useful for time series analysis.
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In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:- ### Pandas Series Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float...